The presence of an airport in a particular region radically influences which businesses operate in the vicinity of the airport and also how such businesses operate. New services are created to fulfill incoming and outgoing passengers' needs, such as, for example, hotels, stores, car rental companies, transport systems, and car parking.
Discovering how flight traffic and scheduling affect this influx of passengers and thus the demand for a particular service over time is of particular interest for any airport dependent business. In the specific case of a parking business, operating in sync with flight departures and arrivals might enable the best possible use of the parking dependencies by, for example, offering lower prices when there is less demand and higher prices when demand increases.
Current parking lot management solutions offer, to some degree, the ability to predict parking demand over a period of time under predetermined circumstances. However, no system currently exists that leverages airport data to accomplish this task. Furthermore, in a non-cooperative scenario, airport authority's ability to access flight passenger data is limited due to privacy or security issues. Thus, only publicly available (but not necessarily free) data sources may be available to accomplish the task.
In a nutshell, the problem faced here is the following: given historical data about previous parking events inside a parking lot in the vicinity of an airport; and publicly available data for past and scheduled departure and arrival flights, 1) how the influence of a particular flight be estimated with respect to parking demand, and 2) how can (at least partial) assignments of parking events be made available to passengers taking the flight?
Positive answers to these questions may allow one to identify and relate parking records to the flight with a degree of certainty. With such information, parking records may be grouped by flight or by destination while in particular offering an understanding regarding the different characteristics of each destination.